Enhanced database information for urban navigation

ABSTRACT

A mobile device includes a receiver to obtain three-dimensional geographic information identifying at least one open area and at least a height associated with at least one object adjacent to the at least one open area, determine at least one pathway in the open area, and determine expected line of sight and expected non-line of sight detections of satellite positioning system (SPS) signals at locations along the pathway based on the three dimensional geographic information. The mobile device includes a processing unit to estimate a location of the mobile device based on application of the expected line of sight detections and the non-line of sight detections to signals acquired at the receiver.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/071,151 issued as U.S. Pat. No. 9,068,841, filed on Nov. 4, 2013,which is a divisional of U.S. patent application Ser. No. 13/619,171issued as U.S. Pat. No. 8,577,597, filed on Sep. 14, 2012, which is adivisional of U.S. patent application Ser. No. 12/489,963 issued as U.S.Pat. No. 8,271,189, filed on Jun. 23, 2009, which claims priority toU.S. Provisional Patent Application Ser. No. 61/100,609, filed on Sep.26, 2008, incorporated by reference herein in its entirety.

BACKGROUND

1. Field

The subject matter disclosed herein relates to electronic devices andmore particularly to methods and apparatuses for use in electronicdevices that perform and/or otherwise support position locationdetermination.

2. Information

Navigation systems and devices and in particular satellite positioningsystems (SPS) such as, for example, the Global Positioning System (GPS)and other like Global Navigation Satellite Systems (GNSS) are becomingmore and more common. An SPS receiver, for example, may receive wirelessSPS signals that are transmitted by a plurality of orbiting satellitesof a GNSS. The SPS signals once received may be processed, for example,to determine a global time, an approximate geographical location,altitude, and/or speed associated for example with a mobile device.

It may be useful to estimate a position location of a mobile device inenvironments, such as, urban regions, where buildings and other likestructures may prevent or inhibit acquisition of enough SPS signals toaccurately estimate a location based solely on traditional techniques.

SUMMARY

Methods and apparatuses are provided for use in one or more electronicdevices that perform and/or otherwise support position locationdetermination.

In accordance with one aspect, methods are provided which may beimplemented in at least one electronic device, to enable the device toaccess satellite signal visibility mask information that is associatedwith at least one satellite enabled to transmit an RF signal. Suchdevice may establish enhanced database information that is associatedwith at least a plurality of point locations. For example, the pointlocations may be associated with a grid pattern that covers ageographical area. The enhanced database information may be establishedbased, at least in part, on the satellite signal visibility maskinformation. Such device may then provide at least a portion of theenhanced database information to at least one mobile device. Forexample, enhanced database information may be transmitted to the mobiledevice over a wireless or wired communication link and/or otherwisetransferred to the mobile device. In certain implementations, forexample, a computer readable medium, such as memory, may be used toprovide enhanced database information to a mobile station.

In certain example implementations, the satellite that transmits the RFsignal may be intended for use in positioning. For example, thesatellite may be a space vehicle (SV) that is part of a satellitepositioning system (SPS). Thus, the satellite signal visibility maskinformation may include SPS signal visibility mask information. Incertain example implementations, the satellite may be intended for oneor more other uses. For example, the satellite may be an SV thatbroadcasts signals in support of a one-way or two-way communicationsystem, and/or the like.

In certain further implementations, the method may be implemented toenable the electronic device to: establish the plurality of pointlocations as being geographically distributed within at least one openarea portion of a region; and establish the signal visibility maskinformation for each of the plurality of point locations based, at leastin part, on at least a portion of three-dimensional geographicalinformation. By way of example, the three-dimensional geographicalinformation may be associated with a geographic information system (GIS)database that specifies the open area and at least a height associatedwith at least one object adjacent to the open area. Here, for example,an open area may include one or more pathways (e.g., a street, etc.),and the object may include one or more buildings and/or otherstructures. In certain implementations, a grid may be aligned with atleast one of an along-pathway direction and/or an across-pathwaydirection. Here, for example, all or portions of a grid may be uniformor non-uniform in at least one direction.

In certain implementations, the signal visibility mask information foreach of the plurality of point locations may include a mask functionthat may be operatively enabled to establish expected azimuth/elevationinformation associated with potential line-of-sight (LOS) signalreception and/or potential non-line-of-sight (NLOS) signal reception.Thus, for example, the enhanced database information may includegeographical coordinates for point locations along with the maskfunction for such point locations. In certain implementations, forexample, the enhanced database information may include geographicalcoordinates for the point locations along with the signal visibilitymask information for such point locations.

In accordance with another aspect, methods are provided which may beimplemented in a mobile device to enable the mobile device to estimatean across-pathway position location by comparing an expected satellitepositioning system (SPS) satellite reception pattern and an observed SPSsatellite reception pattern. For example, a mobile device may be enabledto estimate an along-pathway position location based, at least in part,on at least one SPS signal acquired from at least one SV received LOS.Thus, given accurate enough time source a single SV may be used. Ofcourse a plurality of SV may also be used.

In certain implementations the method may be implemented to enable themobile device to accessing enhanced database information for a pluralityof point locations corresponding to the estimated along-pathway positionlocation. The enhanced database may, for example, also include SPSsignal visibility mask information for such point locations. As such,the method may be implemented to enable the mobile device to determinethe expected SPS satellite reception pattern based, at least in part, onthe SPS signal visibility mask information for each point locations.

In certain implementations the method may be implemented to enable themobile device to read the enhanced database information from at leastone computer-readable medium, and/or receive the enhanced databaseinformation over at least one communication link. In certain examples,the mobile device may be enabled to access an electronic map stored in amemory of the mobile device.

In certain implementations the method may be implemented to enable themobile device to establish an observed SPS satellite reception patternbased, at least in part, on SPS signal reception determination of aLOS/NLOS detector that is operatively enabled within the mobile device.In certain examples, the SPS signal reception determination of theLOS/NLOS detector may be probabilistic.

In certain implementations the method may be implemented to enable themobile device to determine the expected SPS satellite reception patternbased, at least in part, on estimated SPS satellite positions. Forexample, the estimated SPS satellite positions may be determined based,at least in part, on ephemeris information associated with the SPSsatellites.

In certain implementations the method may be implemented to enable themobile device to identify an estimated across-pathway position locationas corresponding to one of the point locations based, at least in part,on at least one probabilistic attribute. For example, a probabilisticattribute may be operatively used in at least one of a Bayesian networkcalculation and/or hidden Markov chain calculation. In certain examples,a probabilistic attribute may be associated with at least one of aprevious estimated across-pathway position location, and/or a previousestimated along-pathway position location. In certain examples, aprobabilistic attribute may be associated with at least one of a timeattribute and/or a measured mobile device movement attribute. In certainexamples, a probabilistic attribute may be associated with at least oneestimated SPS satellite position.

In certain implementations the method may be implemented to enable themobile device to estimate a pseudorange between the satellite and themobile device.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example environment that maybe enabled to implement certain position location determinationtechniques in accordance with an implementation.

FIG. 2 is a schematic block diagram illustrating an exemplary devicethat may be enabled to implement at least a portion of certain positionlocation determination techniques in accordance with an implementation.

FIG. 3A is an illustrative diagram showing an exemplary urbanenvironment having an open area and adjacent objects within whichcertain position location determination techniques in accordance with animplementation may be employed.

FIG. 3B is an illustrative diagram showing a different perspective of aportion of the exemplary urban environment of FIG. 3A.

FIG. 4 is an illustrative diagram showing an exemplary urban environmenthaving an open area and adjacent objects within which certain positionlocation determination techniques in accordance with an implementationmay be employed.

FIG. 5 is an illustrative graph showing example satellite visibilitybased on distance from an object in accordance with an implementation.

FIG. 6 is an illustrative graph of a state transition matrix inaccordance with an implementation.

FIG. 7 is an illustrative graph showing a cross pathway error versustime in accordance with an implementation.

FIG. 8 is an illustrative graph showing an example visibility maskplotted in polar representation in accordance with an implementation.

FIG. 9 is an illustrative a diagram showing certain example signalreception angles with respect to an across-pathway position and arelated LOS/NLOS graph in accordance with an implementation.

FIG. 10 is an illustrative graph showing a further example visibilitymask plotted in polar representation in accordance with animplementation.

FIG. 11 is an illustrative graph showing example satellite visibilitybased on distance from an object in accordance with an implementation.

FIG. 12 is an illustrative graph showing another example visibility maskplotted in polar representation in accordance with an implementation.

FIG. 13 is an illustrative graph showing example satellite visibilitybased on distance from an object in accordance with an implementation.

FIG. 14 is an illustrative graph showing an additional examplevisibility mask plotted in polar representation in accordance with anadditional implementation.

FIG. 15 is an illustrative graph showing an example satellite visibilitybased on distance from an object in accordance with an implementation.

FIG. 16 is a flow-diagram showing an example method for establishingenhanced database information in accordance with an implementation.

FIG. 17 is a flow-diagram showing an example method for using enhanceddatabase information in accordance with an implementation.

FIG. 18 is an example signal processing/transformation flow-diagram inaccordance with an implementation.

DETAILED DESCRIPTION

Non-limiting and non-exhaustive aspects are described with reference tothe following figures, wherein like reference numerals refer to likeparts throughout the various figures unless otherwise specified.

As described in greater detail in subsequent sections, in accordancewith certain example implementations, methods and apparatuses may beprovided for enhancing geographical information that may then be used toestimate a position location of a mobile device in environments, suchas, urban regions, where buildings and other like structures may preventor inhibit acquisition of enough SPS signals in direct visibility toaccurately and/or efficiently estimate one's location based solely ontraditional pseudorange estimation and geometric analysis.

An initial geographical information enhancement technique providedherein may, for example, be performed in advance by one or morecomputing devices and/or other like resources, and at least a portion ofthe resulting enhanced database information may be provided to a mobiledevice and/or other devices that may assist or otherwise support aposition location process associated with the mobile device.

By way of example and introduction, certain methods for establishingenhanced database information may include accessing three-dimensionalgeographical information associated with a region (e.g., a city orportion thereof), and establishing a plurality of point locationsgeographically distributed (e.g., in a grid or other like manner) withinat least one open area portion of the region. SPS signal visibility maskinformation may then be established for each of the plurality of pointlocations based, at least in part, on at least a portion of thethree-dimensional geographical information. Enhanced databaseinformation may then be established, which may be associated with atleast the plurality of point locations (e.g., geographical coordinates)and include some form of SPS signal visibility mask information (e.g.,data and/or functions).

In certain examples, the three-dimensional geographical information maybe extracted from and/or otherwise provided by a geographic informationsystem (GIS) database or the like, which identifies at least one openarea and at least a height associated with at least one object adjacentto the open area. Such a GIS database may identify a footprint or thelike for the object and/or open area. The open area may, for example,include a “pathway”. While the examples herein tend to illustratepathways that are fairly straight (extending on an along-pathwaydirection) and fairly consistent in width (extending on anacross-pathway direction), it should be understood that the techniquesprovided herein are applicable to pathways that vary in shape, e.g., indirection (curved), width (widening/narrowing), and/or elevation.

As used herein, a “pathway” is intended to broadly represent any openspace through which and/or within which a mobile device may be movedabout and that within which the mobile device may have at least someopen sky to enable acquisition of at least some line-of-sight (LOS) SPSsignals from “visible” SPS satellites. By way of example but notlimitation, some pathways may include a street, an expressway, asidewalk, a waterway, a bridge, an overpass, an alley, a median, a park,a plaza, a courtyard, etc.

An object may, for example, include one or more buildings and/or othertypes of structures that may be adjacent to or otherwise positioned insome manner to prevent acquisition of at least some SPS signals from“non-visible” SPS satellites by the mobile device via line-of-sight.Such signals may be received, however, via non-line-of-sight (NLOS) incertain instances (e.g., due to multipath).

As mentioned, in certain examples, at least a portion of the pluralityof point locations may be geographically distributed in a grid or otherlike pattern. At least a portion of the grid may be aligned with atleast one of an along-pathway direction and/or an across-pathwaydirection. In certain implementations all or portions of the grid (e.g.,grid point spacing) may be uniform in all directions, or may benon-uniform in at least one direction.

SPS signal visibility mask information may, for example, include orotherwise operatively specify mask data and/or a mask function thatestablishes and/or may be used to establish expected azimuth/elevationinformation associated with potential LOS SPS signal reception and/orpotential NLOS SPS signal reception for each of the plurality of pointlocations (grid points). Enhanced database information may include, forexample, geographical coordinates for each of the plurality of pointlocations and the mask function and/or other like for each of theplurality of point locations.

All or part of enhanced database information may be stored, for example,on at least one computer-readable medium and/or otherwise provided ormade available to the mobile device and/or other assisting/supportingdevices. In certain examples the enhanced database information may bestored in a memory of the mobile device along with (and/or as part of)an electronic map or other like resource.

To estimate the location of a mobile device using such enhanced databaseinformation, the mobile device (alone or assisted) may, for example,first estimate an along-pathway position location based, at least inpart, on at least two SPS signals acquired from different LOSsatellites.

Enhanced database information may then be accessed and a plurality ofpoint locations corresponding to the estimated along-pathway positionlocation may be identified across-pathway along with corresponding SPSsignal visibility mask information. An expected SPS satellite receptionpattern may then be determined for each of the plurality of pointlocations based, at least in part, on the SPS signal visibility maskinformation. For example, an SPS satellite reception pattern may bedetermined based, at least in part, on the SPS signal visibility maskinformation and estimated SPS satellite positions (e.g., estimated usingephemeris data and mobile device approximate time and position). Incertain example implementations, a mask function may be operativelyenabled to establish expected azimuth/elevation information associatedwith potential LOS SPS signal reception, and/or a potential NLOS SPSsignal reception.

Observed SPS satellite reception patterns may also be established, forexample, based, at least in part, on SPS signal reception determinationof a LOS/NLOS detector or other like function provided within the mobiledevice (and/or assisting device). In certain implementations, asillustrated herein, the SPS signal reception determination of theLOS/NLOS detector may be probabilistic.

An across-pathway position location may then be estimated, for example,by comparing (or otherwise processing) expected SPS satellite receptionpatterns and observed SPS satellite reception patterns. As described ingreater detail in subsequent sections, in certain exampleimplementations an estimated across-pathway position location may beidentified as corresponding to one of the plurality of point locationsbased, at least in part, on at least one probabilistic attribute. Here,for example, one or more probabilistic attributes may be associated withone or more previous estimated across-pathway position locations, and/orone or more previous estimated along-pathway position locations. Aprobabilistic attribute may, for example, be associated with a timeattribute, and/or a measured mobile device movement (e.g., velocity,acceleration) attribute. A probabilistic attribute may, for example, beassociated with at least one estimated SPS satellite position. Certainprobabilistic attribute(s) may, for example, as described in greaterdetail below, be operatively used in and/or otherwise associated withBayesian network, hidden Markov chain, and/or other like calculations.

Thus, as illustrated by certain examples herein, a mobile device (and/orassisting devices) may employ a two-stage position locationdetermination process in which an along-pathway position location mayfirst be estimated based, at least in part, on geometric-basedpseudorange measurements for SPS signals acquired from LOS SPSsatellites. Then an across-pathway position location may be estimatedbased, at least in part, on a comparison of expected LOS/NLOSdeterminations versus observed LOS/NLOS determinations. In certainexample implementations, a “comparison” of expected LOS/NLOSdeterminations versus observed LOS/NLOS determinations may includeprobabilistic analysis based, at least in part, on one or moreprobabilistic attributes.

Attention is now drawn to FIG. 1, which is a block diagram illustratingan environment 100 that includes a mobile device 102 that may beoperatively enabled to acquire SPS signals 104 from at least one SPS106. As shown in this example, mobile device 102 may also (optionally)be operatively enabled to communicate over a wireless link 114 to otherdevices and/or networked devices, such as, e.g., a base station 108, anetwork 110, and/or a server device 112. As shown in FIG. 1, mobiledevice 102 may be located in a region 120, and more specifically withinan open area 122.

Attention is drawn next to FIG. 2, which is a block diagram illustratingcertain features of an exemplary device 200 that may, for example, beincluded in mobile device 102, server 112, and/or other devices, asapplicable, to perform or otherwise support at least a portion of theexample techniques described herein.

Device 200 may, for example, include one or more processing units 202,memory 204, a communication interface 210, an SPS receiver 230, whichmay be operatively coupled in some manner with one or more connections206 (e.g., buses, lines, fibers, links, etc.).

Processing unit 202 may be implemented in hardware, software, or acombination of hardware and software. Thus, for example, processing unit202 may represent one or more circuits configurable to perform at leasta portion of a data computing procedure or process. By way of examplebut not limitation, processing unit 202 may include one or moreprocessors, controllers, microprocessors, microcontrollers, applicationspecific integrated circuits, digital signal processors, programmablelogic devices, field programmable gate arrays, and the like, or anycombination thereof.

Memory 204 may represent any data storage mechanism. Memory 204 mayinclude, for example, a primary memory and/or a secondary memory.Primary memory may include, for example, a random access memory, readonly memory, etc. While illustrated in this example as being separatefrom processing unit 202, it should be understood that all or part of aprimary memory may be provided within or otherwise co-located/coupledwith processing unit 202. Secondary memory may include, for example, thesame or similar type of memory as primary memory and/or one or more datastorage devices or systems, such as, for example, a disk drive, anoptical disc drive, a tape drive, a solid state memory drive, etc.

In certain implementations, secondary memory may be operativelyreceptive of, or otherwise configurable to couple to, computer readablemedium 220. As such, in certain example implementations, the methodsand/or apparatuses presented herein may take the form in whole or partof a computer readable medium 220 that may include computerimplementable instructions 208 stored thereon, which if executed by atleast one processing unit 202 may be operatively enabled to perform allor portions of the example operations as described herein. Such computerimplementable instructions 208 may also be provided by memory 204, asalso illustrated in this example.

Memory 204 may also include enhanced database information 222 that maybe associated with the position location determination operations asdescribed herein.

Communication interface 210 (optional) may, for example, include areceiver 212 and a transmitter 214, and/or combination thereof. Asshown, communication interface 210 may be operatively enabled tocommunicate over a wireless communication link.

A LOS/NLOS detector 232 may be provided in various manners within device200. For example LOS/NLOS detector 232 may be implemented, at least inpart, in SPS receiver 230, as shown. In other examples, LOS/NLOSdetector 232 may be implemented, at least in part, by processor 202 asillustrated by LOS/NLOS detector 232 represented by instructions and/ordata within memory 204. In still other examples, LOS/NLOS detector 232may be implemented, at least in part, as a separate circuit/processingunit, which as shown may be coupled to one or more connections 206.

In accordance with certain aspects, the techniques described in thefollowing example “two-stage” position location implementations mayprovide significant improvement over SPS-only geometric positionlocation techniques and/or may significantly improve positional accuracyand/or efficiency relative to a pathway and specifically with regard toestimating a position across or within the pathway. Such techniques may,for example, be implemented in various methods and/or apparatuses thatmay be associated with a mobile device that may provide accurate “urbancanyon” navigation, and in particular examples, for pedestriannavigation.

Portions of the techniques herein may, for example, be implemented invarious methods and/or apparatuses that may be associated with suchmobile devices and/or one or more other computing platform devices thatmay assist a mobile device with position location determination and/orotherwise establish information that may be used in some manner by themobile device to support position location determination.

As described in greater detail below, certain example methods and/orapparatuses may implement and/or otherwise operatively support a“two-stage” position location determination technique that may employ acombination of geometric positioning analysis based, at least in part,on received SPS signals and probabilistic positioning analysis based, atleast in part, on expected and measured SPS satellite reception patterns(information).

While many of the examples described herein illustrate techniques thatuse geometric analysis to determine along-pathway positions andprobabilistic analysis to determine across-pathway positions, it shouldbe clear that in other examples both along-pathway and across-pathwaypositions may be determined based, at least in part, on probabilisticanalysis. For example, a point grid may be two-dimensional, and aLOS/NLOS probabilistic detector may be used in both along-pathway andacross-pathway directions. This may be useful, for example, atcross-streets, where there may be no well-defined along and acrosspathway directions or even in city squares or parks. Thus, one may use atwo-dimensional point grid technique to “bootstrap” a position atcross-streets. Here, for example, a correct cross-street may beinitially identified by using a geometric position technique (e.g., withat least 4 LOS or near-LOS satellites, identifiable with the maskdatabase, and/or otherwise suitable for a traditional geometricpositioning. A two-dimensional grid may be established at thecrossroads, and a probabilistic analysis may be used subsequently toposition the mobile device as it moves away from the cross-streets.

In certain example implementations a mobile device may include an SPSreceiver and enhanced database information. The SPS receiver may, forexample, include one or more of a GPS, GLONASS, GALILEO, and/or otherlike GNSS receiver that may be enabled to determine pseudorangemeasurements. The SPS receiver and/or other applicable circuitry may beenabled as a “detector” to provide satellite LOS/NLOS detection orsimilar determination. In certain example implementations, such aLOS/NLOS detector may be enabled such that the resulting determinationis not necessarily discrete (e.g., binary absence/presence of LOS).Indeed, for certain example implementations a mobile device may beenabled to accommodate (and possibly even work better) with aprobabilistic LOS/NLOS determination (e.g., 60% chance of LOS, 40%chance of NLOS). It is to note that the same visibility mask databaseinformation may be suited for any GNSS constellation for which aprediction of the satellite location as azimuth and elevation isavailable.

In certain implementations, the enhanced database information mayinclude and/or otherwise be based at least in part on a two-dimensional(2D) database (e.g., possibly with POI (Point-Of-Interest) information),and additional attributes that may be associated with latitude and/orlongitude of location (grid) points and SPS signal visibility maskinformation.

In certain example implementations, some enhanced database informationmay take the form of a special version of an electronic map that mayreside within a mobile device. For example, at least a portion of anelectronic map may be permanently installed in memory and/or other likecomputer-readable medium, downloaded or otherwise provided by anotherdevice (e.g., from a server, etc.) and stored in memory, as may beuseful in a tiling or other like operational manner. Hence, someenhanced database information may reside in a server and/or other likenetwork resources. As mentioned, in certain implementations all orportions of a position location determination may be performed by themobile device, while in other implementations all or portions of aposition location determination may be performed using such a serverand/or other like assisting/supporting networked resource(s).

In certain implementations, enhanced database information may, forexample, be generated (perhaps once, or periodically) from athree-dimensional (3D) urban GIS database or the like with desiredaccuracy level. By way of example but not limitation, an accuracy levelof about 1-2 meters may be adequate for certain areas. Note that incertain implementations, lesser accuracy may impact the usability of thedatabase. This type of three-dimensional database information may, forexample, be available for most cities in the world from a number of GISdatabase resellers. Such three-dimensional database information may, forexample, include a combination of topographic information (e.g., terrainaltitude) along with footprints of buildings and otherstructures/features and their heights. In certain instances, a model maybe employed for such buildings (and/or structures, features, etc.),which uses polygons that correspond to the perimeter of the building,extended with height in vertical dimension. Here, for example, with suchmodels there may be little if any need for “in the field” tuning whichmay allow for a faster time-to-market for such GIS database information.Thus, in certain instances, such a three-dimensional database may onlyneed to be recomputed if the layout of an existing buildingsignificantly changes (e.g., due to renovation or new constructions), orif other buildings (and/or structures, features, etc.) change in somewaywithin a given area.

In certain example implementations, some three-dimensional databaseinformation may be stored in the mobile device and used in real-time (ornear real-time) for position location determination along with a 3Ddatabase that may be used for visibility mask computation. In otherexample implementations, a position computation database may include a2D database that may be simpler and/or require less memory for storage.

Pathways tend to have two directions, namely an along-pathway direction,and an across-pathway direction, which may be perpendicular to thealong-pathway direction. SPS signals from satellites having orbits thatare at least briefly approximately aligned along the pathway directionmay incur little if any multipath effects, may be less likely to beblocked, and/or may have a high LOS probability (along with some delayspread, though). Accordingly, such “aligned” SPS signals from visiblesatellites may contribute the most to an along-pathway positiondetermination.

To the contrary, SPS signals from satellites in azimuth ranges roughlyperpendicular to the pathway direction may suffer the most from directblockages, may have the most cases of NLOS, and/or may experience themost extreme cases of multipath effects. Hence, such “non-aligned” SPSsignals from non-visible satellites may contribute more to anacross-pathway position determination. However, such non-aligned SPSsignals may be so significantly affected by multipath reception thattheir use for obtaining pseudorange measurements may be limited.

In the example implementations herein, the “aligned” SPS signals may beused for along-pathway position location determination and the“non-aligned” SPS signals may be used for across-pathway positionlocation determination in an indirect manner as constrained, in part, bythe along-pathway position determination.

In certain example implementations, one possible indicator of a LOSversus NLOS situation may be the relative amplitude of signal strength.

Enhanced database information may, for example, be established using thefollowing example processes, which may be mapped to the method 1600 asillustrated in the flow diagram example in FIG. 16. With regard tomethod 1600, for example, at block 1602 three-dimensional geographicalinformation for a region may be accessed. At block 1604, at least oneselected open area of the region is associated with a plurality of pointlocations. At block 1606, for each point location, visibility maskinformation may be established based at least in part on the location ofthe point location and the presence or absence of adjacent objects.Method 1600 may, for example, be performed in advance, off-line, etc.,by one or more devices.

Thus, with such in mind, and by way of further example, a device may beenabled to associate a region with a grid pattern (e.g., a rectangulargrid, etc), wherein the grid size may be dependent on a granularity ofthe SPS. Next, the device may be enabled to eliminate all location(grid) points that fall within the perimeters of buildings, structures,and/or other like non-open areas. Next, the device may be enabled tocompute a visibility mask function (and/or mask data) parameterized withazimuth for each remaining grid point (in the open areas). Then, thedevice may be enabled to logically and/or otherwise operatively arrangethis information into a database or other like data structure that mayinclude, for example, grid point latitude and longitude coordinates, anda visibility mask function or at least an approximation of a visibilitymask function, and/or associated mask data, for each grid point (e.g., aminimum elevation angle for LOS visibility, a local azimuth angle,etc.). For example, such enhanced database information may be computedusing a 3D city GIS model, a radio propagation model, and/or the like.

Before illustrating example uses of such enhanced database information,attention is drawn briefly to method 1700 as illustrated in the flowdiagram example in FIG. 17. Method 1700 may, for example, be implementedin whole or in part by a mobile device and/or assisting/supportingdevices.

At block 1702, an attempt may be made to acquire SPS signals from atleast a portion of the theoretically visible SPS satellites. For eachSPS satellite, observed LOS/NLOS detection information may beestablished. At block 1704, an along-pathway position location may beestimated based, at least in part, on SPS signals acquired from at leasttwo satellites. At block 1706, a subset of possibly corresponding pointlocations may be identified based, at least in part, on the estimatedalong-pathway position location. At block 1708, a list or other likedata structure of expected LOS/NLOS satellites may be established foreach corresponding point location. At block 1710, an across-pathwayposition location may be estimated by selecting a corresponding pointlocation based, at least in part, a comparison of expected LOS/NLOSsatellites and observed LOS/NLOS detection information.

By way of further example, such an enhanced database information may beused in real-time or near real-time as part of an iterative mode (e.g.,a steady state operation). Here, for example, a mobile device maydetermine the pathway that it may be located within, and may haveknowledge of one or more previous positions, e.g., within 5-10 m. Basedat least in part on the enhanced database information and a previous(e.g., last) position (possibility propagated by corresponding velocityor other like movement information), the mobile device may at leastestimate the general direction of the pathway.

Based at least in part on satellite ephemeris and/or other likeinformation, the mobile device may at least approximate a time andposition for the (theoretically) visible satellites (e.g., as if in opensky) and azimuth/elevations pairs may be established for thesesatellites. Such information may, for example, be arranged in a table orother like data structure. SPS signal measurements may then becollected, along with corresponding LOS/NLOS determination (e.g.,detector status).

In certain instances, a minimum subset of two satellites may then beselected as being the closest of the general direction of the pathway inazimuth. The pseudorange measurements to these satellites may be used ina single differencing technique, for example, and along with an averagealtitude of the pathway (or an interpolated altitude of grid points) atthe longitudinal pathway position to determine an along-pathway positionfor the mobile device. The along-pathway position location determinationmay also be associated with an uncertainty parameter.

Based at least in part on the along-pathway position locationdetermination and uncertainty parameter, a subset of grid point(s)falling within such an uncertainty domain may be selected and extractedfrom the enhanced database information along with correspondingvisibility mask information. Based at least in part on the visibilitymask information for each selected grid point, and the establishedsatellite azimuth/elevation information (e.g., table), the satellitesmay be classified or otherwise identified as expected LOS or NLOS.Depending on the parallax experienced from various grid positions, theresulting lists may be different for each grid point. A comparison/matchbetween expected LOS (e.g., from the grid) satellites and observed LOSsatellites may be made, and based at least in part thereon, anacross-pathway position location determination made based on a bestmatch. The combination of along-pathway and across-pathway positiondeterminations may then be returned and/or otherwise provided as a finalestimated position location for the mobile device.

In certain implementations, an initial position location determinationmay be made to provide a starting point for further positioningdetermination. Here, for example, the SPS signaling environment maypresent shadowing patterns or the like in parallel pathways, perhapseven with similar building heights, that may be very similar, and assuch may not be reliable enough to determine with desired confidencewhich pathway the mobile device is starting from. In accordance withcertain implementations, a geometric computational approach may, forexample, be employed until either an uncertainty domain encompasses gridpoints with significantly different shadowing patterns, and/or until atleast a threshold number (e.g., four) of SPS signals from satelliteswith LOS have been acquired. For example, a position computation may bemade based at least in part on the received SPS signal and used in somemanner to seed a probabilistic determination. Such a situation mayarise, for example, as a mobile device reaches pathway crosses,intersections, and/or other like potentially more open areas and/orpossible direction changing areas wherein the threshold number of SPSsignals from satellites with LOS may be available. The position may thenbe propagated according to Markov chains (e.g., to take advantage ofposition history) and/or the like and an along-pathway/across-pathwayalgorithm.

To further illustrate such techniques, attention is now drawn to FIGS.3A and 3B, which are related and graphically illustrate certainalong-pathway positions and across-pathway positions for a mobile devicewithin a pathway located between buildings, and potential SPS signalreception. FIG. 3A shows a horizontal view looking down on a pathway300. FIG. 3B shows a vertical view from within pathway 300 with abuilding 302-1 in the background.

More specifically, FIG. 3A shows pathway 300 between building 302-1(e.g., on what will be referred to as the left hand side) and buildings302-2 and 302-3 (e.g., on what will be referred to as the right handside). Various possible positions 304 (point locations) for a mobiledevice within pathway 300 are illustrated. For example, across-pathwaypositions 304-1, 304-2, 304-3, 304-4, and 304-5 are illustrated in awidthwise association, which in this example is perpendicular to thepathway direction. As illustrated by the dashed ovals in this example,adjacent across-pathway positions may be associated with certainalong-pathway positions, 306-1, 306-2 and 306-3 that have a lengthwiseassociation, which in this example is parallel to the pathway direction.Here, the dashed oval may also illustrate that some along-pathwaypositional uncertainty may actually extend into other pathways.

In FIG. 3A, SPS satellites 106-1 and 106-2 may be visible, LOSsatellites as their signals 308-1 and 308-2, respectively, may beacquired by a mobile device at point location 304-3, for example. SPSsatellites 106-3 and 106-4 may not be visible, and considered to be NLOSsatellites for a mobile device at point location 304-3, for example.Here, for example, signals from SPS satellite 106-3 may suffer frommultipath from surface 332 of building 302-2, and signals from SPSsatellite 106-3 may be substantially or completely blocked by surface330 of building 302-1 for a mobile device at point location 304-3.

In FIG. 3B, by way of further example two signals are illustrated asbeing transmitted from LOS satellites 106-1 and 106-2 and available to amobile device if located at about any point location of along-pathwayposition 306-2. FIG. 3B illustrates elevation angles for such SPSsignals and potential vertical constraints associated with building302-1 that may affect signal reception. As illustrated, an along-pathwayposition location determination may be estimated based, at least inpart, on at least the two SPS signals.

FIG. 4 is similar to FIGS. 3A and 3B, and illustrates a vertical viewfrom the pathway with building 302-1 on the left hand side and building302-2 on the right hand side. As illustrated in the example, building302-1 may have a height 406 of 90 meters (e.g., about twenty floors),building 302-2 may have a height 408 of 22.5 meters (e.g., about fivefloors), pathway 300 may have a width 402 of 23 meters, and a grid stepdistance 404, between adjacent point locations 304, may be 5 meters. Ofcourse, unless stated otherwise, all measurements presented herein areby way of example only.

As illustrated in FIG. 4 by the directional lines (e.g., labeled a, b,c, d, e) extending outward at certain angles from each point location304, some SPS signals may be blocked or otherwise subjected to theshadowing pattern associated with buildings 302-1 and/or 302-2. Table410 includes columns aligned below the illustrated point locations androws for particular SPS signals that may or may not be received in LOSfrom a given satellite (e.g., identified as PRN 1, 2, 3, 4, and 5) foreach of the illustrated point locations. Here, for example, a “1” islisted if a particular SPS signal may be received in LOS at a pointlocation, and a “0” is listed if a particular SPS signal may not bereceived in LOS (e.g., may or may not be received NLOS) at a pointlocation. Thus, for example, a SPS signal from PRN 2 may be received inLOS at or nearby point locations 304-3, 304-4, and/or 304-5, but may notbe received LOS at or nearby point locations 304-1 and/or 304-2, whilean SPS signal from PRN 4 may be received in LOS only at or nearby pointlocation 304-1. Also, for example, SPS signals from PRN 1 and/or PRN 5may not be received in LOS at or nearby any of the point locations304-1, . . . , 304-5.

As further illustrated in FIG. 4 by arrows 412 pointing to the columnsassociated with point locations 304-3 and 304-4, in certain situationstwo or more point locations may share the same pattern of LOS/NLOSsignal reception. Here, for example, at point locations 304-3 and 304-4SPS signals from PRN 1, PRN 4 and PRN 5 may be received in NLOS whileSPS signals from PRN 2 and PRN 3 may be received in LOS.

In accordance with certain aspects of the present description, furtherpoint location determination/discrimination may be established usingcertain exemplary probabilistic techniques, for example, as described ingreater detail below.

Furthermore, in accordance with certain aspects a mobile device may beenabled to apply a pseudorange measurement single difference using atleast the two SPS signals to reduce and/or eliminate receiver clockerror that may be inaccurately known since it may not be possible attimes to make a full position location determination (estimate) alongwith receiver clock error determination.

One potential classification concern is determining which pre-computedshadowing pattern in the grid may provide the best match with anobserved one. Such concern may, for example, be addressed based, atleast in part, using a Bayesian inference engine that may be associatedwith a hidden Markov chain for handling time sequence constraints.

In accordance with certain aspects of the present description,techniques are provided for use in determining (estimating) a pointlocation based, at least in part, on a combination of Bayesian networksand hidden Markov chains to provide probabilistic position estimationsbased on SPS signals. In an example implementation, a grid may belogically established with respect to the general direction of thepathway (e.g., within a given area). Each grid point may be associatedwith a theoretical LOS/NLOS pattern that may, for example, beestablished and/or otherwise obtained from a visibility mask at the gridpoint azimuth/elevation (Az/El) and an approximate satellite locationexpressed in local coordinate system (Az/El), e.g., which may becomputed on the fly.

A LOS/NLOS reference pattern may be established and/or otherwiseobtained based, at least in part, on a satellite direct visibilityanalysis and/or the like. FIG. 5 shows a graph 500 of satellitevisibility versus distance from a building (to the left), in which thevertical axis lists a range of PRN numbers and the horizontal axis liststhe distance in meters (m). Graph 500 is associated therefore with aplot of a cross section of a pathway, which in this example is 23 mwide, and is bounded by 20 m-high buildings on both sides. Lines 502,504, 506, 508, 510, and 512 represent where in the cross section a givensatellite may be received in direct LOS. For example, counted ascross-distance from the left building, as illustrated by line 502 thesatellite PRN 21 may be in LOS visibility from the left building (0 m)up to about 12 m, as illustrated by line 502 the satellite PRN 22 may bein LOS visibility from about 14 m to the right building (23 m), and asillustrated by line 504 the satellite PRN 18 may be in LOS visibilityfrom about the left building (0 m) to the right building (23 m). Theseexample visibility plots may be sampled at regular or various gridpoints, e.g., at 5 m, 10 m, 15 m and 20 m from the left building. Thus,a LOS visibility list may be extracted at each grid point from thevisibility plot.

To better formalize the process, let D_(Grid) be a vector of griddistances from the left building in meters:D_(Grid)=[5 10 15 20]

Let, L_(Grid)(t_(n-1)) be a mass probability of being located at a gridpoint at time t_(n-1) (e.g., before the first application of thisalgorithm). This may be understood as the starting or initial position.If nothing is known a priori, it may arbitrarily be evenly distributedover the whole width of the pathway. In the example, let's assume thatthe user of the mobile device starts in a position at the rightmostposition in the pathway (e.g., on a sidewalk adjacent the building onthe right side). Thus,L _(Grid)(t _(n-1))=[0 0 0.1 0.9]

A complete list of pathway-visible satellites, e.g., in LOS conditionfrom at least one point of the pathway (not the full list of satellitesabove the horizon in an open sky hypothesis), may be given byRefSatList:RefSatList=[6,7,14,18,21,22]^(T)

A LOS visibility list at each grid point may be described as an array,LOSPATTERN, with columns representing for each location in the grid theLOS visibility of all satellites, and rows representing the LOSvisibility for a particular satellite at all grid points. Here, rowindexes may correspond to RefSatList indexes, and column indexes maycorrespond to D_(grid) indexes. Below, for example, is a spatialsampling based on graph 500:

${LOSPATTERN} = \begin{bmatrix}1 & 0 & 0 & 0 \\1 & 1 & 0 & 0 \\0 & 0 & 1 & 1 \\1 & 1 & 1 & 1 \\1 & 1 & 0 & 0 \\0 & 0 & 1 & 1\end{bmatrix}$

The measurements P_(LOS) provided by a receiver for the across-pathwayposition location may be a vector of LOS probabilities for eachsatellite in the RefSatList list. Column index may refer to thesatellite in RefSatList with the same index. In certain exampleimplementations, measurements may be associated with a LOS/NLOSdetector, and/or established using other like means. This may notnecessarily indicate that multipath is present or absent in a compoundsignal, but that at least LOS may be present. Thus,P_(LOS)=[0.05 0.1 0.8 0.9 0.1 0.8]

Here, the example is contrived, as the measurements do not come from anactual receiver, but essentially describe what is believed one mightexperience when located at about 15 meters from the left building. Inthis example, the probability value has been chosen to be higher than0.5 when visible, and the farthest from the LOS/NLOS transition, theclosest to certainty 1. For example, the satellite PRN 18 may be seen invisibility all across the pathway, therefore it has high LOS probability(0.9). The satellite PRN 14 and PRN 22 which may be seen in visibility,and appear farther from a LOS/NLOS transition, have been assigned a highLOS probability of 0.8. The NLOS satellite probabilities have beenassigned following a similar reversed logic. For example, probabilitymay be lower than 0.5 when NLOS, and closer to 0 when farther from aLOS/NLOS transition.

The following vector is the probability of location of the receiver ateach grid point, conditioned with the observation pattern P_(LOS). Thisis the observation (o) probability given a specific location (l).

Applying the Bayes rulep(l|o)·p(o)=p(o|l)·p(l)for a single grid point, provides:

${p\left( {l❘o} \right)} = {\frac{{p\left( {o❘l} \right)} \cdot {p(l)}}{p(o)} = {K \cdot {p\left( {o❘l} \right)} \cdot {p(l)}}}$

Where p(o|l) is the probability to observe o given the location l p(l)is the a-priori probability of being at the grid point 1 before updatingwith measurements p(l|o) is the probability of being at l, given o isobserved.

$K\left( {= \frac{1}{p(o)}} \right)$may be considered as a normalizing constant, and adjusted so thatΣ_(Grid)P(D_(Grid)|o)=1.

Thus, generalizing over all grid points, provides:p(o|Grid)=P _(LOS)·LOSPATTERN=[1.1500 1.1000 2.5000 2.5000]

The Bayes formula, simultaneously applied to the whole grid becomesp(GRID(t _(n))|o)=K·p(o|GRID)·p(GRID(t _(n))).

With K chosen such as Σ_(Grid)p(GRID|o)=1, p(GRID(t_(n))) may beestimated using a hidden markov chain, and using the last estimatedacross-pathway position location. Here, for example, the idea is thatthe user of a mobile device may be more likely to follow a path in thealong-pathway direction, rather than the across-pathway. The transitionmatrix between cross grid at t_(n-1) and t_(n),p(D_(Grid)(t_(n))|D_(Grid)(t_(n-1))) may, for example, be expressed as:

${p\left( {{{GRID}\left( t_{n} \right)}❘{{GRID}\left( t_{n - 1} \right)}} \right)} = \left\lfloor \begin{matrix}0.7 & 0.2 & 0.1 & 0.0 \\0.2 & 0.6 & 0.2 & 0.00 \\0.00 & 0.2 & 0.6 & 0.2 \\0.00 & 0.1 & 0.2 & 0.7\end{matrix} \right\rfloor$

Note this is just an arbitrary example, which may be tuned and/orotherwise modified/adapted as needed in practice.

Such may be used to capture that in practice pedestrians have much moretendency to stay on course (e.g., same distance from the left sidebuilding), and also to stay on the left hand side or right hand sidesidewalk if there already. If one were in the middle of the pathway,there may be about equal chance of movement towards the left or theright side (perhaps relatively expediently, as in crossing the pathway).

With such in mind, FIG. 6 illustrates a graph 600 of a state transitionmatrix. FIG. 6 includes buildings 302-1 (left hand side) and 302-2(right hand side) and located there between point locations 602-1,602-2, 602-3, and 602-4.

In this example, it may be assumed that the location probabilitydistribution at time t_(n-1) was obtained by application of the sameformula at time tn-1, hence:p(GRID(t _(n-1)))=[0 0 0.10.9].

The a-priori probability to be at each point of the grid is:p(GRID(t _(n)))=p(GRID(t _(n-1)))·p(GRID(t _(n))|GRID(t _(n-1)))

In this numerical case, therefore:p(GRID(t _(n)))=[0 0.11 0.24 0.65]p(GRID|o)=K·p(o|GRID(t _(n)))×p(GRID(t _(n)))where “x” denotes the component by component multiplication. Thus,p(GRID|o)=0.4263·[1.1500 1.1000 2.5000 2.5000]×[0 0.11 0.24 0.65]−[00.0516 0.2558 0.6927]

A final reported across-pathway position location may be themathematical expectation of location when given observation:

${E\left( {{GRID}❘o} \right)} = {{\sum\limits_{l^{\prime} \in {GRID}}{l^{\prime} \cdot {p\left( {l^{\prime}❘o} \right)}}} = {D_{Grid} \cdot {p\left( {{GRID}❘o} \right)}}}$where the ‘.’ (dot) operator denotes a dot productE(GRID|o)=[5 10 15 20]·[0 0.0516 0.2558 0.6927]=18.20

FIG. 7 shows a graph 700 illustrating across-pathway error versus time,with an estimated distance (m) from the building on the left hand sideon the vertical axis and discrete time steps on the horizontal axis.Lines 702, 704 and 706 are shown, which illustrate a convergence to thesame across-pathway value from arbitrary starting points, continuouslyapplying the same measurement vector P_(LOS)=[0.05 0.1 0.8 0.9 0.1 0.8].For example, line 702 corresponds to an initial point p(GRID(t₀))=[0 00.1 0.9] or an expected cross distance of 19.5 meters.

Here, for example, the “convergence” speed may be directly related tothe Markov transition matrix numbers. It is conceivable, however, thatthe Markov transition matrix may be different for car navigation versuspedestrian navigation. Thus, the transition values may, for example, beadjusted according to a time interval size and to an average linearvelocity of the receiver, and/or other like factors, e.g., as may beestablished or otherwise obtained from a longitudinal SPS algorithm.

The error of the final across-pathway distance value from thetheoretical value of 15 meters may be due, at least in part, on to thesomewhat arbitrary method used to construct the example measurementvector.

Some visibility mask examples will now be described and illustrated. Asimple example of a straight pathway with constant building height alonga significant enough portion of the pathway may yield a characteristicvisibility mask shape. FIG. 8 is a graph 800 illustrating an example ofa visibility mask 802 plotted in polar representation, with pathwayorientation at 45 degrees from true North, for a pathway of 23 meterswide, with a left hand building at 90 meters high, and right handbuilding at 20 meters high. In FIG. 8, the value of the satellitevisibility mask is plotted along the radial direction, with the centercorresponding to ninety degrees and the circumference of the circlecorresponding to zero degrees. The point of view is taken at 10 metersfrom the building on the left hand side. As illustrated, it is clearthat the directions at 45 degrees and 225 degrees corresponding toalong-pathway directions are obstacle-free down to 0 degrees ofelevation. The azimuth towards the highest building, at 315 degrees, hasthe highest elevation mask at about 82 degrees.

To represent such a shape with the reduced (perhaps minimum) number ofpoints in a database, for example, one may reconstruct the shapemathematically, such that only a point at a given azimuth and elevationis necessary on each side of the pathway in this case, along with thegeneral orientation of the pathway.

If a pathway has multiple buildings with varying heights and lengths,including cross pathways, the same type of representation may apply,with multiple transitions in the mask curve that may be encodedseparately. Thus, it is not believed that such a visibility maskapproach should create any significant limitations in the generality ofthe concepts presented herein.

If a satellite that might be used to make a distinction between twocontiguous partitions is missing, then both regions may be combined intoa single (possibly unique) one, however, the accuracy may be reduced.

If some pathways are parallel and have similar building heights and/orsimilar widths, then the visibility patterns may be significantlysimilar. This may increase the difficulty in determining which pathwaythe mobile device (receiver) is actually on. Here, as described above,it may be useful to consider positional history to track the trajectory.An initial position location determination may be performed moreaccurately when the receiver is at crossroads, e.g., where there aremore LOS satellites to unambiguously determine the first crossroad.

As mentioned, a LOS/NLOS detector may be implemented to provide aLOS/NLOS detection. Such a detector may not be perfectly accurate, andin certain implementations, a soft decision may actually be preferable.The Bayesian networks should still work correctly. An exampleimplementation of a detector may be based on heuristics about the widthof the correlation peak. The width of the correlation peak, or moreaccurately its stretch compared to the nominal width may be an indirectmeasure of the Delay Spread (e.g., the difference of propagation delaybetween the shortest path and the longest path). There tends to be atight correlation between Delay Spread and presence of multipath, and alooser correlation between presence of multipath and NLOS. Thus, forexample, a small Delay Spread may represent a loose indication of a LOSsituation, while a larger Delay Spread may represent a NLOS situation.In certain implementations, an atmospheric pressure sensor may be usedas an alternate source for use in estimating altitude, for example, ifaltitude information is not available for a grid point in a database

FIG. 9 includes a diagram 900 illustrating certain example signalreception angles with respect to an across-pathway position 901 (here, atrue location) and a related graph 920. Note that, for illustrationpurposes, the example in diagram 900 depicts PseudoRange (PR) instead ofPseudoRange single differences between satellites as it should be, andonly considers the vertical component of the PR difference, instead ofthe slant difference as it should be. Here, building 302-2 is shownalong with an actual real path 902 of an SPS signal reflected off ofwall 332, a modeled real path 904 of a (PR_(measured)) SPS signal, andan expected PR (PR_(estimated)) 906. Line 908 is the local verticaldirection and line 912 represents an innovation error difference basedon PR_(measured) and PR_(estimated). For example, an innovation errormay be related to the difference between the PR_(measured) andPR_(estimated). Graph 920, for example, shows a NLOS probability 922 andLOS probability 924 plotted with respect to the innovation error.

Thus, for example, at a precise location of each grid point a mobiledevice may be enabled to compare, the measured PR versus expected PRsingle differences. Depending on whether the discrepancy is larger orsmaller than the thermal noise (actually twice the thermal noise becauseof single differencing), a decision may be based on a proportion of LOSand NLOS, with a smooth transition from LOS to NLOS decision. The othersatellite for forming the single difference may be beneficially chosenat a sufficiently high angle to reduce (possibly minimize) the chancesof contribution of multipath to single difference, and/or to simplifythe single difference interpretation. Thus, a LOS/NLOS set ofmeasurements may be obtained that are conditioned to the a-priorilocation, and a measurement vector may be recalculated for each gridpoint.

The quality of an across-pathway position location determination may, incertain implementations, be dependent on the granularity of the skyangular sampling, and thus of the number of satellites in the sky. In afirst order approximation of uniform spatial distribution of allsatellites through the sky, and an approximately identical number ofsatellites per constellation, the across-pathway position locationaccuracy may be twice the accuracy of single constellation case, if wejointly use all satellites from two constellations. Thus, it may beuseful for the receiver to be enabled to acquire SPS signals from aplurality of GNSS, and/or other like systems, and to jointly use allmeasurements from several GNSS constellations in the probabilisticpositioning part of the method.

In certain example implementations, the histogram of across-streetdistance with no satellite visibility change over a large sample oflocations, pathway widths, and satellite configurations may provide a50% percentile estimate of cross-path distance without satellitevisibility change. This number determines the potential accuracy of theacross path positioning. Thus, one may, for example, sample (e.g.,choose a grid width or spacing, etc.) at about twice this 50%percentile. In certain examples, therefore a spacing of about fivemeters across a pathway may be sufficient. Claimed subject matter is nothowever limited and may include grid spacing that is less than orgreater than 5 meters, and/or uniform or non-uniform grid patterns,and/or the like.

As the geometry does not change significantly with a translation alongthe pathway, it is believed that a sampling grid of about 50 metersalong-pathway may also be sufficient in certain example implementations.Claimed subject matter is not however limited and may includealong-pathway grid spacing that is less than or greater than 50 meters,and/or uniform or non-uniform grid patterns, and/or the like.

As mentioned, in certain implementations, the grid spacing may be alsoand/or alternatively be non-uniform (e.g., along and/or across thepathway), as the visibility mask may not significantly evolve alongcertain pathways. Such implementations may be useful in controllingand/or otherwise reducing the size of the enhanced database information,with a reasonable accuracy, and/or associated processing requirements.

With regard to the size of the enhanced database information, in oneexample, a metropolitan region that may be roughly contained in a squareof about: 10 km²×10 km²=1.10⁸ m². With a hypothetical ratio of 40%building footprint area and 60% open areas, the area to cover in theexample enhanced database information may be approximately1.10⁸×0.6=6.10⁷ m².

With a grid spacing (e.g., here a uniform tile size) of 5×50 m²=250 m²(5 meters grid spacing across pathways, and 50 m along pathways), thetotal number of grid points will be 240,000 points. Assuming fiveconspicuous azimuth/elevation points per grid point and a resolution ofone degree is azimuth and elevation so each point can be coded in twobytes, the minimum enhanced database information size (without overhead)may be approximately 240,000×5×2 bytes=2.4 Mbytes. With an assumedarbitrary overhead of about 10% (compressed latitude, longitude gridinformation, plus other possible formatting information), the totalenhanced database information size may be estimated to be about 2.64Mbytes. Of course this is just an example in which about 0.026 Bytes/m²of information may be reasonable for certain exemplary dense urbanenvironments.

For a single step of an exemplary Bayesian Algorithm there may be aminimum number of grid points to consider. For example, if each gridpoint has a visibility mask associated with five azimuth/elevationdirections, in an erroneous SPS position, a circular uncertainty errorof about 50 m circular, or a number of grid points of about,

${{G\; P\; N} \cong {\frac{\pi \cdot R^{2}}{A}\left( {1 - F} \right)}},$where GPN is the Grid Point Number, R is the SPS uncertainty circle(e.g., at 95% (in meters)), A is the grid tile size (in m²), and F isthe building area/total area fill ratio. Thus, in this example, the GPNmay be about 18 grid points.

In accordance with certain aspects of the present description, themethods and apparatuses may use other propagation-related parametersthat may be available in the enhanced database information.

For example, other types of propagation-related information may be usedin the enhanced database information in complement and/or insubstitution to a visibility mask, and applied to an exemplaryprobabilistic position computation technique. In certain instances, tobe particularly effective, it may be desirable that such otherparameter(s) be independent of the motion of the satellites (e.g.,permanent), relatively stable over long periods (e.g., several months),represented in enhanced database information with the minimum number ofbytes, have sufficient variability/contrast between two contiguous gridpoints to be usable for position determination, easily predictable by aradio propagation simulator and/or a 3D Database, and/or easilymeasurable in a receiver (e.g., with sufficient accuracy). Such apotential candidate parameter may, for example, include a multipathdelay spread.

Attention is drawn next to FIG. 10, which is a graph 1000 illustrating afurther example of several visibility masks plotted in polarrepresentation, with pathway orientation at 0 degrees from true North,for a pathway of 23 meters wide, with a left hand building at 5 metershigh, and right hand building at 5 meters high. Hence, this is a “low”building height partitioning example.

Here, five superimposed visibility masks are illustrated, namely, mask1002 at 1 meter, mask 1004 at 6 meters, mask 1006 at 11 meters, mask1008 at 16 meters, and mask 1010 at 21 meters from a building on theleft hand side of the pathway. Also illustrated in graph 1000 areseveral example satellites (shown as stars identified by adjacent PRNnumbers).

The satellite space may be partitioned, for example, as follows:

Offset from left building PRN 1 m 6 m 11 m 16 m 21 m 3 0 0 1 1 1 6 1 1 11 0 7 1 1 1 1 0 14 0 1 1 1 1 16 0 0 0 1 1 18 1 1 1 1 1 21 1 1 1 1 0 22 01 1 1 1 24 1 1 1 0 0 26 1 1 1 0 0 29 1 1 0 0 0 32 0 0 1 1 1

Each transversal offset may have a (possibly unique) visibilitysignature that may be used to identify in which region of the pathwaythe receiver may be located.

Hence, FIG. 11 shows a corresponding graph 1100 of satellite visibilityversus distance from a building (to the left), in which the verticalaxis lists a range of PRN numbers and the horizontal axis lists thedistance in meters. As shown, line 1102 is associated with PRN 32, line1104 is associated with PRN 29, line 1106 is associated with PRN 26,line 1108 is associated with PRN 24, line 1110 is associated with PRN22, line 1112 is associated with PRN 21, line 1114 is associated withPRN 18, line 1116 is associated with PRN 16, line 1118 is associatedwith PRN 14, line 1120 is associated with PRN 7, line 1122 is associatedwith PRN 6, and line 1124 is associated with PRN 3.

Attention is drawn next to FIG. 12, which is a graph 1200 illustratinganother example of several visibility masks plotted in polarrepresentation, with pathway orientation at 0 degrees from true North,for a pathway of 23 meters wide, with a left hand building at 20 metershigh, and right hand building at 20 meters high. Hence, this is a“medium” building height partitioning example.

Here, four superimposed visibility masks are illustrated, namely, mask1202 at 1 meter, mask 1204 at 6 meters, mask 1206 at 11 meters, and mask1208 at 16 meters from a building on the left hand side of the pathway.Also illustrated in graph 1000 are several example satellites (shown asstars identified by adjacent PRN numbers).

Hence, FIG. 13 shows a corresponding graph 1300 of satellite visibilityversus distance from a building (to the left), in which the verticalaxis lists a range of PRN numbers and the horizontal axis lists thedistance in meters. As shown, line 1302 is associated with PRN 22, line1304 is associated with PRN 21, line 1306 is associated with PRN 18,line 1308 is associated with PRN 14, line 1310 is associated with PRN 7,and line 1312 is associated with PRN 6.

Attention is drawn next to FIG. 14, which is a graph 1400 illustratingan additional example of several visibility masks plotted in polarrepresentation, with pathway orientation at 0 degrees from true North,for a pathway of 23 meters wide, with a left hand building at 90 metershigh, and right hand building at 20 meters high. Hence, this is a “mixed(medium/high)” building height partitioning example.

Here, four superimposed visibility masks are illustrated, namely, mask1402 at 1 meter, mask 1404 at 6 meters, mask 1406 at 11 meters, and mask1408 at 16 meters from the taller building on the left hand side of thepathway. Also illustrated in graph 1000 are several example satellites(shown as stars identified by adjacent PRN numbers).

Hence, FIG. 15 shows a corresponding graph 1500 of satellite visibilityversus distance from a building (again to the left), in which thevertical axis lists a range of PRN numbers and the horizontal axis liststhe distance in meters. As shown, line 1502 is associated with PRN 21,line 1504 is associated with PRN 18, line 1506 is associated with PRN 7,and line 1508 is associated with PRN 6.

Attention is drawn next to FIG. 18, which shows a signalprocessing/transformation flow 1800 to further illustrate certainaspects of the example techniques described herein.

Here, a three-dimensional GIS or other like database 1802 may beprocessed to produce grid points 1804. Visibility mask 1808 may beprocessed based, at least in part, on grid points 1804 and signalpropagation model 1806. An initial processing stage 1812 is shown, whichmay, for example, be performed by processing at one or more computingplatforms specially established based on instructions. Initialprocessing stage 1812 may, for example, produce enhanced database 1810.A subset of grid points 1820 may be extracted from enhanced database1810 based, at least in part, on along-pathway position locationestimate 1818. Along-pathway position location estimate 1818 may begenerated by a first location position estimator 1816, which may processacquired SPS signals 1814. For example, first location positionestimator 1816 may implement a geometric analysis process based, atleast in part, on pseudorange measurements from acquired SPS signals.Expected LOS/NLOS satellites masking information 1822 may be determinedfor each grid point represented by subset of grid point 1820 based, atleast in part, on estimated satellite positions 1824. Observed LOS/NLOSSPS satellite masking information 1826 may be determined based, at leastin part, on SPS signals 1814. For example, a LOS/NLOS detector maygenerate observed LOS/NLOS SPS satellite signals 1826. A second locationposition estimator 1828 may process observed LOS/NLOS SPS satellitemasking information 1826 and expected LOS/NLOS satellite maskinginformation 1822 to generate estimated across-pathway position location1830. For example, second location position estimator 1828 may implementa probabilistic analysis process based, at least in part, on variousprobabilistic attribute signals.

The example position location determination techniques described hereinmay be used in stand alone or otherwise substantially autonomous mobiledevices, and/or for mobile devices enabled to provide additionalfunctionality, for example, through the use of various wirelesscommunication networks such as a wireless wide area network (WWAN), awireless local area network (WLAN), a wireless personal area network(WPAN), and so on. The term “network” and “system” are often usedinterchangeably. A WWAN may be a Code Division Multiple Access (CDMA)network, a Time Division Multiple Access (TDMA) network, a FrequencyDivision Multiple Access (FDMA) network, an Orthogonal FrequencyDivision Multiple Access (OFDMA) network, a Single-Carrier FrequencyDivision Multiple Access (SC-FDMA) network, and so on. A CDMA networkmay implement one or more radio access technologies (RATs) such ascdma2000, Wideband-CDMA (W-CDMA), and so on. Cdma2000 includes IS-95,IS-2000, and IS-856 standards. A TDMA network may implement GlobalSystem for Mobile Communications (GSM), Digital Advanced Mobile PhoneSystem (D-AMPS), or some other RAT. GSM and W-CDMA are described indocuments from a consortium named “3rd Generation Partnership Project”(3GPP). Cdma2000 is described in documents from a consortium named “3rdGeneration Partnership Project 2” (3GPP2). 3GPP and 3GPP2 documents arepublicly available. A WLAN may be an IEEE 802.11x network, and a WPANmay be a Bluetooth network, an IEEE 802.15x, or some other type ofnetwork. The techniques may also be used for any combination of WWAN,WLAN and/or WPAN.

A mobile device may receive SPS signals from various satellites or thelike, which may be from a Global Positioning System (GPS), Galileo,GLONASS, NAVSTAR, GNSS, a system that uses satellites from a combinationof these systems, or any SPS developed in the future, each referred togenerally herein as a “Satellite Positioning System” (SPS).

Furthermore, the methods and apparatuses described herein may be usedwith position location determination systems that utilize pseudolites ora combination of satellites and pseudolites. Pseudolites may includeground-based transmitters that broadcast a PN code or other ranging code(e.g., similar to a GPS or CDMA cellular signal) modulated on an L-band(or other frequency) carrier signal, which may be synchronized with SPStime. Each such transmitter may be assigned a unique PN code so as topermit identification by a remote receiver. Pseudolites may be useful insituations where SPS signals from an orbiting satellite might beunavailable, such as in tunnels, mines, buildings, urban canyons orother enclosed areas. Another implementation of pseudolites is known asradio-beacons. The term “satellite”, as used herein, is intended toinclude pseudolites, equivalents of pseudolites, and possibly others.The term “SPS signals”, as used herein, is intended to include SPS-likesignals from pseudolites or equivalents of pseudolites.

As used herein, a mobile device refers to a device that may acquire atleast SPS signals for use in position location determination. In certainexamples, a mobile device may include a cellular or other wirelesscommunication device, personal communication system (PCS) device,personal navigation device, a vehicle mountable navigation device, atracking device, Personal Information Manager (PIM), Personal DigitalAssistant (PDA), laptop or other suitable mobile device which may becapable of receiving wireless communications. A mobile device may, forexample, include devices which communicate with a personal navigationdevice (PND), such as by short-range wireless, infrared, wirelineconnection, or other connection—regardless of whether satellite signalreception, assistance data reception, and/or position-related processingoccurs at the device or at the PND. Certain mobile devices may, forexample, include all devices, including wireless communication devices,computers, laptops, etc. which are capable of communication with aserver, such as via the Internet, WiFi, or other network, and regardlessof whether satellite signal reception, assistance data reception, and/orposition-related processing occurs at the device, at a server, or atanother device associated with the network. Any operable combination ofthe above may also be considered a “mobile device”.

The methodologies described herein may be implemented by various meansdepending upon the application. For example, these methodologies may beimplemented in hardware, firmware, software, or a combination thereof.For a hardware implementation, one or more processing units may beimplemented within one or more application specific integrated circuits(ASICs), digital signal processors (DSPs), digital signal processingdevices (DSPDs), programmable logic devices (PLDs), field programmablegate arrays (FPGAs), processors, controllers, micro-controllers,microprocessors, electronic devices, other electronic units designed toperform the functions described herein, or a combination thereof.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory of a mobile device, and executed by a processing unitof the mobile device. Memory may be implemented within a processing unitand/or external to the processing unit. As used herein the term “memory”refers to any type of long term, short term, volatile, nonvolatile, orother memory and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

If implemented in software, the functions that implement themethodologies or portions thereof may be stored on and/or transmittedover as one or more instructions or code on a computer-readable medium.A computer-readable medium may take the form of an article ofmanufacture. A computer-readable medium may include computer storagemedia and/or communication media including any medium that facilitatestransfer of a computer program from one place to another. A storagemedia may be any available media that may be accessed by a computer orlike device. By way of example but not limitation, a computer-readablemedium may comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that can be used to carry or store desired program code inthe form of instructions or data structures and that can be accessed bya computer. Disk and disc, as used herein, includes compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable medium.

“Instructions” as referred to herein relate to expressions whichrepresent one or more logical operations. For example, instructions maybe “machine-readable” by being interpretable by a machine for executingone or more operations on one or more data objects. However, this ismerely an example of instructions and claimed subject matter is notlimited in this respect. In another example, instructions as referred toherein may relate to encoded commands which are executable by aprocessing unit having a command set which includes the encodedcommands. Such an instruction may be encoded in the form of a machinelanguage understood by the processing unit. Again, these are merelyexamples of an instruction and claimed subject matter is not limited inthis respect.

While some portions of the detailed description have been presented interms of algorithms or symbolic representations of operations on binarydigital signals stored within a memory of a specific apparatus orspecial purpose computing device or platform in the context of thisparticular specification, the term specific apparatus or the likeincludes a general purpose computer once it is programmed to performparticular functions pursuant to instructions from program software.Algorithmic descriptions or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processing orrelated arts to convey the substance of their work to others skilled inthe art. An algorithm is here, and generally, is considered to be aself-consistent sequence of operations or similar signal processingleading to a desired result. In this context, operations or processinginvolve physical manipulation of physical quantities. Typically,although not necessarily, such quantities may take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals or the like. It should be understood, however, that all ofthese or similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent in this description, terms such as “processing”,“computing”, “calculating”, “enabling”, “identifying”, “detecting”,“obtaining”, “estimating”, “associating”, “receiving”, “transmitting”“acquiring”, “providing”, “storing”, “accessing”, “determining”, or thelike refer to actions or processes of a specific apparatus, such as aspecial purpose computer or a similar special purpose electroniccomputing device. In the context of this specification, therefore, aspecial purpose computer or a similar special purpose electroniccomputing device may be capable of manipulating or transforming signals,typically represented as physical electronic or magnetic quantitieswithin memories, registers, or other information storage devices,transmission devices, or display devices of the special purpose computeror similar special purpose electronic computing device.

While there has been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from the central concept described herein.

Therefore, it is intended that claimed subject matter not be limited tothe particular examples disclosed, but that such claimed subject mattermay also include all aspects falling within the scope of appendedclaims, and equivalents thereof.

What is claimed is:
 1. A method comprising, at a mobile device:obtaining three-dimensional geographic information identifying at leastone open area and at least a height associated with at least one objectadjacent to the at least one open area; determining at least one pathwayin said open area; determining expected line of sight and expectednon-line of sight detections of satellite positioning system (SPS)signals at locations along said pathway based, at least in part, on saidthree dimensional geographic information; acquiring one or more signalsat a receiver; and estimating a location of said mobile device based, atleast in part, on application of said expected line of sight detectionsand expected said non-line of sight detections to said acquired signals.2. The method of claim 1, wherein said at least one area comprises astreet and wherein said three-dimensional geographic informationindicates a height of a building adjacent to said street.
 3. The methodof claim 1, wherein said SPS signals comprise signals transmitted by oneor more transmitters in a global navigation satellite system.
 4. Themethod of claim 1, wherein obtaining said three-dimensional geographicinformation comprises obtaining at least some of said three-dimensionalgeographic information from messages received from a server.
 5. Themethod of claim 1, wherein said expected line of sight and non-line ofsight detections of SPS signals at locations along said pathway arebased, at least in part, on estimated probabilities of acquisition ofsaid SPS signals at said locations.
 6. The method of claim 1, andfurther determining said expected non-line of sight detections of SPSsignals at locations along said pathway further comprises determiningSPS signals transmitted from one or more transmitter locations on a lineof sight to said locations along said pathway that are obscured by saidat least one object.
 7. The method of claim 1, and further determiningsaid expected line of sight detections of SPS signals at locations alongsaid pathway further comprises determining SPS signals transmitted fromone or more transmitter locations on a line of sight to said locationsalong said pathway that are not obscured by said at least one object. 8.A mobile device comprising: a receiver; and at least one processing unitoperatively coupled to said receiver and enabled to obtainthree-dimensional geographic information identifying at least one openarea and at least a height associated with at least one object adjacentto said at least one open area; determine at least one pathway in saidopen area; determine expected line of sight and expected non-line ofsight detections of satellite positioning system (SPS) signals atlocations along said pathway based, at least in part, on said threedimensional geographic information; and estimate a location of saidmobile device based, at least in part, on application of said expectedline of sight detections and expected said non-line of sight detectionsto signals acquired at said receiver.
 9. The mobile device of claim 8,wherein said at least one area comprises a street and wherein saidthree-dimensional geographic information indicates a height of abuilding adjacent to said street.
 10. The mobile device of claim 8,wherein said SPS signals comprise signals transmitted by one or moretransmitters in a global navigation satellite system.
 11. The mobiledevice of claim 8, wherein said at least one processing unit is furtherenabled to obtain at least some of said three-dimensional geographicinformation from messages received from a server.
 12. The mobile deviceof claim 8, wherein said expected line of sight and non-line of sightdetections of SPS signals at locations along said pathway are based, atleast in part, on estimated probabilities of acquisition of said SPSsignals at said locations.
 13. The mobile device of claim 8, whereinsaid at least one processing unit is further enabled to determine saidexpected non-line of sight detections of SPS signals at locations alongsaid pathway by determining SPS signals transmitted from one or moretransmitter locations on a line of sight to said locations along saidpathway that are obscured by said at least one object.
 14. The mobiledevice of claim 8, wherein said at least one processing unit is furtherenabled to determine said expected line of sight detections of SPSsignals at locations along said pathway by determining SPS signalstransmitted from one or more transmitter locations on a line of sight tosaid locations along said pathway that are not obscured by said at leastone object.
 15. An article comprising a non-transitory computer readablemedium having computer implementable instructions stored thereon whichif implemented by one or more processing units operatively enables saidone or more processing units to: obtain three-dimensional geographicinformation identifying at least one open area and at least a heightassociated with at least one object adjacent to said at least one openarea; determine at least one pathway in said open area; determineexpected line of sight and expected non-line of sight detections ofsatellite positioning system (SPS) signals at locations along saidpathway based, at least in part, on said three dimensional geographicinformation; and estimate a location of said mobile device based, atleast in part, on application of said expected line of sight detectionsand expected said non-line of sight detections to signals acquired at areceiver.
 16. The article of claim 15, wherein said at least one areacomprises a street and wherein said three-dimensional geographicinformation indicates a height of a building adjacent to said street.17. The article of claim 15, wherein said SPS signals comprise signalstransmitted by one or more transmitters in a global navigation satellitesystem.
 18. The article of claim 15, wherein said instructions ifimplemented by said one or more processing units further operativelyenable said one or more processing units to obtain at least some of saidthree-dimensional geographic information from messages received from aserver.
 19. The article of claim 15, wherein said expected line of sightand non-line of sight detections of SPS signals at locations along saidpathway are based, at least in part, on estimated probabilities ofacquisition of said SPS signals at said locations.
 20. The article ofclaim 15, wherein said instructions if implemented by said one or moreprocessing units further operatively enable said one or more processingunits to determine said expected non-line of sight detections of SPSsignals at locations along said pathway by determining SPS signalstransmitted from one or more transmitter locations on a line of sight tosaid locations along said pathway that are obscured by said at least oneobject.
 21. The article of claim 15, wherein said instructions ifimplemented by said one or more processing units further operativelyenable said one or more processing units to determine said expected lineof sight detections of SPS signals at locations along said pathway bydetermining SPS signals transmitted from one or more transmitterlocations on a line of sight to said locations along said pathway thatare not obscured by said at least one object.
 22. An apparatuscomprising: means for obtaining three-dimensional geographic informationidentifying at least one open area and at least a height associated withat least one object adjacent to said at least one open area; means fordetermining at least one pathway in said open area; means fordetermining expected line of sight and expected non-line of sightdetections of satellite positioning system (SPS) signals at locationsalong said pathway based, at least in part, on said three dimensionalgeographic information; means for acquiring one or more signals at areceiver; and means for estimating a location of said mobile devicebased, at least in part, on application of said expected line of sightdetections and expected said non-line of sight detections to saidacquired signals.
 23. The apparatus of claim 22, wherein said at leastone area comprises a street and wherein said three-dimensionalgeographic information indicates a height of a building adjacent to saidstreet.
 24. The apparatus of claim 22, wherein said SPS signals comprisesignals transmitted by one or more transmitters in a global navigationsatellite system.
 25. The apparatus of claim 22, wherein said means forobtaining said three-dimensional geographic information comprises meansfor obtaining at least some of said three-dimensional geographicinformation from messages received from a server.
 26. The apparatus ofclaim 22, wherein said expected line of sight and non-line of sightdetections of SPS signals at locations along said pathway are based, atleast in part, on estimated probabilities of acquisition of said SPSsignals at said locations.
 27. The apparatus of claim 22, wherein saidmeans for determining said expected non-line of sight detections of SPSsignals at locations along said pathway further comprises means fordetermining SPS signals transmitted from one or more transmitterlocations on a line of sight to said locations along said pathway thatare obscured by said at least one object.
 28. The apparatus of claim 22,wherein said means for determining said expected line of sightdetections of SPS signals at locations along said pathway furthercomprises means for determining SPS signals transmitted from one or moretransmitter locations on a line of sight to said locations along saidpathway that are not obscured by said at least one object.